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Although the Super Bowl was played just three months ago, the draft hasn’t
taken place yet and training camps aren’t open, it’s still not too early to
start thinking about the NFL if you play at Pinnacle Sports!

The season kicks off in less than 5 months from now when the Super Bowl
Champion Pittsburgh Steelers host the Miami Dolphins in a Thursday night
match-up. Pinnacle Sports has already posted lines on every game in week 1
courtesy of our in-house team of odds makers who’ve handicapped the games and
created the lines. But as a player, how does one handicap the first week’s
games and find an edge this far out?

As a starting point, look at the relationship between season wins and the
first week point spreads. An average team would expect to win 50% of the time or
8 games in a 16-game regular season. In essence, the expected season wins for a
team is just a power ranking.

How many games would you expect a team to win, if it was a 3-point favorite
for each game? First you need to convert the fair no-vig moneyline (ML) into a
"percentage chance of winning" for each game. For favorites, that is
the (ML quote / (ML - 100)) * 100. If the fair no-vig moneyline for a 3-point
favorite is -145/+145, we would expect the 3-point favorite to win (-145
/(-145-100)) * 100 = 59% of the time. If we knew a team would be a 3-point
favorite for every game, we would expect it to win 16*0.59 games, or about 9.5
games.

Although not a perfect science, you can use this to convert season win lines
into a game line for the first week. For every ½ game better the favorite is
for season wins, it should give up an additional 1 point on the spread at a
neutral site. If a 9.5-win team played an 8-win team, the 9.5-win team would be
a 3-point favorite on a neutral field. After that, add 3 points for home field
advantage, so the 9.5 win team would be a 6-point favorite at home, or a Pick’em
on the road.

Then set a “baseline” using games from the prior year, in this case the
2005-2006 NFL season. While some people will simply start with the number of
games a team won in the previous season, more sophisticated bettors use the “Pythagorean
Theorem” for football. This formula reduces the affects of lucky and/or close
wins, and gives a team more credit for blowouts and consistently solid
performances.

For example, consider the 2005 Tampa Bay Buccaneers regular season record of
11-5, with 300 points scored for and 274 points scored against. Instead of
simply using their win/loss record, using the Pythagorean Theorem for football
it assumes games won = (PF^2) / (PF^2+PA^2) * 16, where PF=points for and
PA=points against.

Using the Pythagorean Theorem for football, the Buccaneers’ baseline would
be calculated as 300*300/(300*300+274*274) * 16 which gives an expectation of
8.7 games. This suggests that Tampa Bay was very lucky to win 11 games and if
they played the same season with the same roster, 9 wins would be much more
likely.

Conversely using the same formula, we can see that last year Green Bay’s
record undervalued the team. The Packers finished at 4-12, with 298 points for
and 344 points scored against. Their baseline would be 298*298/(298*298+344*344)
* 16 = 6.9 games, nearly 3 full games better than their record from last year.

The Pythagorean Theorem is a starting point in your analysis that gives you a
leg up over handicappers who don’t use it. Although originally derived by Bill
James for MLB, its applications have extended across many sports by changing the
exponent (2 for NFL, 1.8 for MLB, and 16.5 for the NBA).

Another adjustment you can make to the 2005 season wins baseline is the “reversion
to the mean”. Basically this means that no matter what a team does in a
previous season, it tends to move toward winning 50% of its games the following
season. Bad teams aren’t quite as bad as people remember them and the
dynasties eventually fade. A general rule of thumb is to move the baseline
season wins about ½ a game toward 8 for baselines between 5.5-10.5, or a full
game towards 8 for very good/bad teams outside that range.

Once the baseline is calculated, you need to consider roster changes. Is a
team peaking or rebuilding? If a team has several older players retiring and
being replaced with younger, inexperienced players, this suggests the team could
be in a rebuilding stage. Younger players tend to contribute less in the first
few years and in a majority of cases, the affect of the draft on a team can be
ignored and you can instead focus on trades/free agents acquired. If many
starters are inexperienced at the top level, that team can be expected to fare
worse the next year, but gradually improve afterwards.

If a team’s roster is fairly stable, you generally expect the team to do as
well or better the following year. On teams with a low turnover rate, the focus
of the off-season is to add talented veterans to positions lacking experience
and hope for an immediate impact on the team.

Adding depth (e.g. a journeyman backup QB, or a fourth cornerback) will have
less of an impact, but also lowers the downside variance. A team that should
have put more effort into its backup quarterbacks is the 2005 New York Jets. The
Jets fared well in 2004 under Chad Pennington with a reasonable QB passer rating
of 91. In 2005, the 1-1 Jets lost Pennington and backup Jay Fiedler for the
season in the third game. They won only 3 of the next 14 games with Brooks
Bollinger and Vinny Testaverde struggling at QB, with passer ratings of 59 and
73, respectively.

Analyzing roster changes is a very subjective matter. For each one evaluated
(some handicappers ignore all changes involving third-string players or deeper),
try to consider how that will affect the team’s play. If a team has a poor
defense and an average offense, defensive changes will have a bigger impact –
the defense simply has more room for improvement.

Once season win expectations are completed, then set the line for each game.
As in the earlier example, take the difference between the two teams in season
wins, multiply by 2, and add 3 for the home field advantage. If your numbers
suggest a play, we are open for business at Pinnacle Sports on NFL Week 1. With
our 10-cent line on NFL openers, you’ll even get up to 50% better value
compared to other sportsbooks when they finally get around to posting their NFL
openers...

How have the sharps bet the early week 1 NFL openers?

Miami Dolphins +5.5 at Pittsburgh Steelers

We opened the game at Miami +6.5, and took multiple limit bets from sharps on
the dog. If you faded the Super Bowl Champion for the first 2 weeks from 1985 to
2005, you would be 28-13. So any opening number will draw sharp versus public
betting.

Cincinnati Bengals +3.5 at Kansas City Chiefs

Our opener of Cincinnati +2.5 saw moderate lopsided betting on the Chiefs,
driving the number onto and past the “3”. Using the Pythagorean Theorem,
Cincinnati appears to have over-performed in 2005, where 9.5 wins would be more
reflective of the team that actually went 11-5. The early betting tends to agree
that the number on the Bengals was too high.

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